Rice is one of the agricultural products which main commodity in Indonesia. Supporting factors that play a very important role efforts to increase rice production are superior varieties. Superior varieties have characteristics similar another. Thus, farmers must choose used through classification process determine appropriate type rice. At this stage, three methods used: bagging, boosting, and ...

Abstract—Vote-boosting is a sequential ensemble learning method in which individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined as a function of the disagreement rate of the current ensemble predictions for that particular instance. Experiments using the symmetric beta distribution as th...

Recently, there has been a notable tendency towards employing ensemble learning methodologies in assorted areas of engineering, such as hydrology, for simulation and prediction purposes. The diversity techniques available implementation hydrological sciences led to the development utilization different strategies implementation. This review paper explores refers advancement methods, including r...

We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multiple training sets. Other approaches, such as Randomized C4.5 apply randomization in selecting a test at a given node of a tree. Then there are approaches, such as random forests and random subspaces, that apply randomizat...

Résumé. En classification supervisée, de nombreuses méthodes ensemblistes peuvent combiner plusieurs hypothèses de base afin de créer une règle de décision finale plus performante. Ainsi, il a été montré que des méthodes comme le bagging ou le boosting pouvaient se révéler intéressantes, tant dans la phase d’apprentissage qu’en généralisation. Dès lors, il est tentant de vouloir s’inspirer des ...

We experimentally evaluate bagging and seven other randomization-based approaches to creating an ensemble of decision-tree classifiers. Unlike methods related to boosting, all of the eight approaches create each classifier in an ensemble independently of the other classifiers in the ensemble. Bagging uses randomization to create multiple training sets. Other approaches, such as those of Dietter...

Handling missing attribute values is an important issue for classiier learning, since missing attribute values in either training data or test (unseen) data aaect the prediction accuracy of learned classiiers. In many real KDD applications, attributes with missing values are very common. This paper studies the robust-ness of four recently developed committee learning techniques, including Boost...